A Wavelet Transform Based Feature Extraction and Classification of Cardiac Disorder

Sumathi, S.; Beaulah, Lilly; Vanithamani, R.

doi:10.1007/s10916-014-0098-x

Cited by 28 publications

(19 citation statements)

References 9 publications

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“…(10)), and soft thresholding (Eq. (8)) was the cut-off method for high frequency components [11][12][13]. Daubechies wavelets are the extreme phase wavelets.…”

Section: Discrete Wavelet Transform To Denoise Temporal Fluorescence mentioning

confidence: 99%

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Wavelet analysis of cardiac optical mapping data

Xiong

Nattel

et al. 2015

Computers in Biology and Medicine

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“…(10)), and soft thresholding (Eq. (8)) was the cut-off method for high frequency components [11][12][13]. Daubechies wavelets are the extreme phase wavelets.…”

Section: Discrete Wavelet Transform To Denoise Temporal Fluorescence mentioning

confidence: 99%

“…All four types of wavelets have been used in biomedical signal and image processing [15][16][17][18][19]. Symlet functions have the disadvantage of generally slower processing than other wavelet-based methods including Daubechies filtering [13][14][15][16][17].…”

Section: Discrete Wavelet Transform To Denoise Temporal Fluorescence mentioning

confidence: 99%

Wavelet analysis of cardiac optical mapping data

Xiong

Nattel

et al. 2015

Computers in Biology and Medicine

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“…Cardiovascular disease (CVD) is caused by disorders of the heart and blood vessels [1]. It includes diseases that affect the circulatory system such as; coronary heart disease (heart attacks), cerebrovascular disease (stroke), raised blood pressure (hypertension), congestive heart failure, congenital heart disease, and heart failure.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Aydin¹,

Güler²

2016

IJCEE

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Heart Rate Variability (HRV) signal which is providing information about variation between consecutive heartbeats has been employed for extracting the parameter related to Congestive Heart Failure (CHF) from Electrocardiography (ECG). There are several studies on HRV analysis and CHF however most of the studies were performed by text-based methods. The main objective of this work is not only search to extract parameters but also to accomplish the difficulties in the text based program. This study presents graphical programming language to investigate features of HRV. Graphical User Interface (GUI) has been developed in LabVIEW to create some method for extraction features related to CHF. HRV are analyzed in the time and frequency domains. Parameters related to the time and frequency domains are derived for healthy ECG and ECG signals having Congestive Heart Failure. Several parameters which are necessary for classification algorithms are obtained.

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“…Mustafa et al [18] used EMD to analyze anesthesia EEG signals to extract the time-frequency features in the delta-theta bands. Sumathi et al [19] employed the Symlet wavelet transform to extract the attenuation factor and basic frequency parameters of the ECG signals for cardiac arrhythmias. Sen et al [20] studied 41 feature parameters from 20 attribute algorithms in four categories.…”

Section: Introductionmentioning

confidence: 99%

Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism

Lin

Wang

2015

J Med Syst

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Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation-inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were correlated. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy ratios of the wave of an alcoholic observer to its refereed total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16 s. For normal EEG signals, more than 1 % of the total energy ...

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A Wavelet Transform Based Feature Extraction and Classification of Cardiac Disorder

Cited by 28 publications

References 9 publications

Wavelet analysis of cardiac optical mapping data

Wavelet analysis of cardiac optical mapping data

Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism

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